Method for generating one or more control signals for operating an analogue quantum computer

Abstract

There is presented a method for generating one or more control signals for operating an analogue quantum computer, AQC. The AQC comprises a plurality of position-controlled matter-particles; wherein at least one control signal is for controlling an electromagnetic, EM, source for imparting EM radiation to the matter-particles. The method comprises using an artificial intelligence, AI, method to generate at least one control signal for the EM source; wherein the AI method is developed using at least one of: data output from applying previous EM radiation to the AQC; or, data output from emulating, on a classical computer, the application of EM radiation to the AQC.

1 . A computer-implemented method for generating one or more control signals for operating an analogue quantum computer, AQC, for determining a solution to combinatorial problem; the AQC comprising a plurality of position-controlled matter-particles; wherein at least one control signal is for controlling an electromagnetic, EM, source for imparting EM radiation to the matter-particles;
 the method comprising using an artificial intelligence, AI, computer model to generate the at least one control signal; the AI computer model configured to:
 I) receive input data associated with the combinatorial problem; and, 
 II) output data for generating the at least one control signal, wherein the output data is associated with one or more characteristics of the EM radiation; 
   the AI computer model being developed using at least one of:
 a) data output from applying previous EM radiation to the matter particles of the AQC; or 
 b) data output from emulating, on a classical computer, the application of EM radiation to the matter particles of the AQC. 
   
     
     
         2 . The method as claimed in  claim 1  wherein:
 the AQC comprises a quantum system formed from the plurality of position-controlled matter particles; 
 the at least one control signal is configured to interact with the quantum system such that the quantum system is governed by a single Hamiltonian operator. 
 
     
     
         3 . The method as claimed in  claim 2  wherein the AI computer model is for operating the AQC for solving a graph problem, wherein a plurality of the matter-particles represents the nodes and/or the edges of the graph. 
     
     
         4 . The method as claimed in  claim 2  wherein the AI computer model comprises at least one of:
 i) a model trained using a supervised learning algorithm; 
 ii) a model trained using a reinforcement learning algorithm. 
 
     
     
         5 . The method as claimed in  claim 4 ,
 wherein the AI computer model is for operating the AQC for solving a graph problem, wherein a plurality of the matter-particles represents the nodes and/or the edges of the graph;   the method further comprising:   inputting data associated with the graph problem and the graph into the model; and,   outputting from the model, data associated with the control signals for controlling the EM source.   
     
     
         6 . The method as claimed in  claim 5  wherein the EM radiation imparted on the matter particles is for transitioning the matter-particles between a first atomic state and a second state; the data output from the model comprises data associated with at least one of:
 i) Rabi frequency associated with the first and second atomic states; 
 ii) detuning of the EM source wavelength from the transition frequency of first and second atomic states; 
 iii) the phase of the EM source. 
 
     
     
         7 . The method as claimed in  claim 5  wherein the data associated with the control signals for controlling the EM source, that are output from the model, comprise data representing a time-sequence of values of the EM radiation. 
     
     
         8 . The method as  claim 7  wherein:
 i) the first state is a ground state; 
 ii) the second state is a Rydberg state. 
 
     
     
         9 . The method as claimed in  claim 1 , wherein the AQC comprises a neutral atom quantum computer. 
     
     
         10 . The method as claimed in  claim 3 , wherein:
 the graph is a first graph associated with a first spatial configuration of the position-controlled matter-particles:   the method comprising training the AI computer model using training data comprising any one or more of:   A) data associated with one or more second graphs different from the first graph;   B) data associated with the first graph and a second spatial configuration of the position-controlled matter-particles that is different to the first spatial configuration of the position-controlled matter particles.   
     
     
         11 . The method as claimed in  claim 10  wherein the training data comprises a plurality of data subsets, each subset comprising:
 data associated with the graph problem and at least one of the second graphs and, 
 data associated with the EM radiation for the said second graph for imparting to the AQC for encoding the graph problem onto the matter particles. 
 
     
     
         12 . The method as claimed in  claim 10  wherein training the AI computer model comprises selecting a machine learning method from a plurality of machine learning methods based on a criteria associated with any one or more of:
 i) graph size; 
 ii) graph order; 
 iii) number of matter-particles to represent the graph problem; 
 iv) matter-particle position configuration; 
 v) EM pulses for each second graph in the training data set: 
 vi) the problem to be solved; 
 vii) time budget to generate the training data set; 
 ix) to which class the graph belongs. 
 
     
     
         13 . The method as claimed in  claim 10 , wherein training the AI computer model comprises:
 i) setting a register of the matter particles wherein the positions of the matter particles in the register is associated with the nodes of the second graph;   ii) running a plurality of quantum simulations on the AQC and/or emulations of the AQC, each simulation or emulation comprising: a) inputting a training EM signal; and, b) determining which matter particles are excited after the input of the training EM signal; each quantum simulation or emulation comprising a different training EM signal to the training EM signals of the other quantum simulations or emulations;   iii) selecting one or more of the quantum simulations and/or emulations from the plurality of quantum simulations and/or emulations based on the said determinations of excited matter particles;   iv) training the AI computer model using the selected one or more quantum simulations and/or emulations.   
     
     
         14 . A method for training an AI computer model for use with an analogue quantum computer, AQC; the AQC comprising a plurality of position-controlled matter-particles;
 the method comprising:   i) setting a register of the matter particles wherein the positions of the matter particles in the register are associated with the nodes of a graph;   ii) running a plurality of quantum simulations on the AQC and/or classical computer emulations of the AQC; each simulation or emulation comprising:   a) inputting a training Electro Magnetic, EM, signal to the matter particles of the AQC or emulated AQC; and,   b) determining which matter particles are excited after the input of the training EM signal; each quantum simulation or emulation comprising a different EM signal to the EM signals of the other quantum simulations or emulations;   iii) selecting one or more of the quantum simulations and/or emulations from the plurality of quantum simulations and/or emulations based on the said determinations of excited matter particles;   iv) training the AI computer model using the selected one or more quantum simulations and/or emulations.   
     
     
         15 . A system for generating one or more control signals for operating an analogue quantum computer, AQC, for determining a solution to combinatorial problem; the AQC comprising a plurality of position-controlled matter-particles; wherein at least one control signal is for controlling an electromagnetic, EM, source for imparting EM radiation to the matter-particles;
 the system comprising a processor and a memory, the memory comprising instructions that are executable by the processor to use an artificial intelligence, AI, computer model to generate the control signal;   the AI computer model configured to:
 A) receive input data associated with the combinatorial problem; and, 
 B) output data for generating the at least one control signal, wherein the output data is associated with one or more characteristics of the EM radiation; 
   the AI computer model being developed using at least one of:
 i) data output from applying previous EM radiation to the matter particles of the AQC; or, 
 ii) data output from emulating, on a classical computer, the application of EM radiation to the matter particles of the AQC. 
   
     
     
         16 . The system of  claim 15  further comprising the AQC. 
     
     
         17 . The system of  claim 16  wherein:
 the AQC comprises a quantum system formed from the plurality of position-controlled matter particles; 
 the at least one control signal is configured to interact with the quantum system such that the quantum system is governed by a single Hamiltonian operator. 
 
     
     
         18 . The system of  claim 17  wherein the AI computer model comprises at least one of:
 i) a model trained using a supervised learning algorithm; 
 ii) a model trained using a reinforcement learning algorithm. 
 
     
     
         19 . The system as claimed in  claim 18  wherein the AI computer model is for operating the AQC for solving a graph problem, wherein a plurality of the matter-particles represents the nodes and/or the edges of the graph;
 the AI computer model further configured to: 
 receive input data associated with the graph problem and the graph into the model; and, 
 output from the model, data associated with the control signals for controlling the EM source. 
 
     
     
         20 . The system of  claim 16  wherein the AQC comprises a neutral atom quantum computer.